I’m looking for an efficient algorithm that for a space with known height, width and length, given a fixed radius R, and a list of points N, with 3-dimensional coordinates in that space, will find all the points within a fixed radius R of an arbitrary point on the grid. This query will be done many times with different points, so an expensive pre-processing/sorting step, in exchange for quick queries may be worth it. This is a bit of a bottleneck step of an application I’m working on, so any time I can cut off of it is useful
Things I have tried so far:
-The naive algorithm, iterate over all points and calculate distance
-Divide the space into a grid with cubes of length R, and put the points into these. That way, for each point, I only have to ever query the immediate neighboring buckets. This has a significant speedup
-I’ve tried using the manhattan distance as a heuristic. That is, within the buckets, before calculating a distance to any point, use the manhattan distance to filter out those that can’t possibly be within radius R (that is, those with a manhattan distance of <= sqrt(3)*R). I thought this would offer a speedup, as it only needs addition instead of multiplication, but it actually slowed the program down by a little bit
EDIT: To compare the distances, I use the squared distance to eliminate having to use a sqrt function.
Obviously, there will be some limit on how much I can speed this up, but I could use any suggestions on things to try now.
Not that it probably matters on the algorithmic level, but I’m working in C.
You may get a speed benefit from storing your points in a k-d tree with three dimensions. That will give you searchs in O(log n) amortized time.